U.S. patent application number 12/160739 was filed with the patent office on 2009-07-30 for method and device for determining the speed of a moving entity.
This patent application is currently assigned to JACOBS UNIVERSITY BREMEN GGMBH. Invention is credited to Mostafa Zaman Afgani, Stephane Beauregard, Harald Haas.
Application Number | 20090189813 12/160739 |
Document ID | / |
Family ID | 36591373 |
Filed Date | 2009-07-30 |
United States Patent
Application |
20090189813 |
Kind Code |
A1 |
Haas; Harald ; et
al. |
July 30, 2009 |
METHOD AND DEVICE FOR DETERMINING THE SPEED OF A MOVING ENTITY
Abstract
A method and a corresponding device determines the speed of a
moving entity carrying at least two antennas for receiving a
transmission signal the antennas being displaced at a predetermined
distance. In order to provide a more simple and accurate method
which can be used with different transmission signals the method
includes the steps of: receiving a transmission signal by the
antennas, determining signal characteristics from the transmission
signal as received by the determining a time offset between the
reception of the transmission signal at the antennas by comparing
the signal characteristics determined for the antennas, and
determining the speed of the moving entity from the determined time
offset, the distance of the antennas and the direction of movement
of the moving entity relative to the arrangement of the
antennas.
Inventors: |
Haas; Harald; (Edinburgh,
GB) ; Afgani; Mostafa Zaman; (Edinburgh, GB) ;
Beauregard; Stephane; (Bremen, DE) |
Correspondence
Address: |
BROMBERG & SUNSTEIN LLP
125 SUMMER STREET
BOSTON
MA
02110-1618
US
|
Assignee: |
JACOBS UNIVERSITY BREMEN
GGMBH
Bremen
DE
|
Family ID: |
36591373 |
Appl. No.: |
12/160739 |
Filed: |
December 8, 2006 |
PCT Filed: |
December 8, 2006 |
PCT NO: |
PCT/EP2006/011853 |
371 Date: |
March 27, 2009 |
Current U.S.
Class: |
342/384 |
Current CPC
Class: |
G01S 11/026
20130101 |
Class at
Publication: |
342/384 |
International
Class: |
G01S 11/02 20060101
G01S011/02 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 11, 2006 |
EP |
06000446.2 |
Claims
1. Method of determining the speed of a moving entity carrying at
least two antennas for receiving a transmission signal, said
antennas being displaced at a predetermined distance, comprising
the steps of: receiving a transmission signal by the at least two
antennas, determining signal characteristics from the transmission
signal as received by the at least two antennas, determining a time
offset between the reception of the transmission signal at the at
least two antennas by comparing the signal characteristics
determined for the at least two antennas, and determining the speed
of the moving entity from the determined time offset, the distance
of the at least two antennas and the direction of movement of the
moving entity relative to the arrangement of the at least two
antennas.
2. Method as claimed in claim 1, wherein the moving entity carries
two antennas, arranged along a line parallel to the direction of
regular movement of said moving entity, wherein for each antenna
signal characteristics are determined from the received
transmission signal and wherein the determined signal
characteristics are compared to determine said time offset.
3. Method as claimed in claim 1, wherein the moving entity carries
a plurality of antennas, arranged in a common plane or a common
sphere, wherein for each antenna signal characteristics are
determined from the received transmission signal and wherein the
signal characteristics are compared to determine the time offsets
between the reception of the transmission signal at the plurality
of antennas.
4. Method as claimed in claim 3, wherein said antennas are arranged
at the corners of a polygon having equally long side lengths, at
equal distances along the circumference of a circle or equi-spaced
on the surface of a sphere.
5. Method as claimed in claim 3, wherein the direction of movement
of the moving entity relative to the arrangement of the at least
two antennas is determined from the differences of time offsets
determined for the plurality of antennas.
6. Method as claimed in claim 1, wherein the comparison of the
signal characteristics is performed continuously.
7. Method as claimed in claim 1, wherein signal characteristics
determined from a portion of the signal as received by the antenna
located foremost in movement direction are stored as reference
characteristics and wherein the same signal characteristics are
determined from the transmission signals received by the other
antennas and compared to the reference characteristics to determine
the time offset(s).
8. Method as claimed in claim 1, wherein said signal
characteristics are an envelope signal, amplitude peak values,
energy values, delay, multipath delay characteristics, maximum
delay, root-means-square (RMS) of delay, maximum Doppler frequency,
Doppler spectrum or spectral characteristics of the transmission
signal.
9. Method as claimed in claim 1, wherein an envelope signal is
determined as signal characteristic from the transmission signal as
received by the at least two antennas and used for determining the
time offset between the reception of the transmission signal at the
at least two antennas by comparing the envelope signals determined
for the at least two antennas.
10. Method as claimed in claim 9, wherein at least a portion of the
envelope signal determined from the transmission signal received by
the antenna located foremost in a movement direction is stored as a
reference portion and the other envelope signals are compared to
this reference portion to determine the time offset(s).
11. Method as claimed in claim 1, wherein the signal
characteristics are compared using a pattern or signal matching
method.
12. Method as claimed in claim 1, wherein the direction of movement
of the moving entity relative to the arrangement of the at least
two antennas is determined by use of a navigation system or an
acceleration sensor.
13. Method as claimed in any claim 1, wherein the at least two
antennas are aligned with the direction of movement of the moving
entity.
14. Method as claimed in any claim 1, wherein said at least two
antennas are identical.
15. Method as claimed in claim 1, wherein said at least two
antennas are adapted for receiving transmission signals of a, in
particular of a GSM, UMTS, WiMax, WiFi system, or of a digital
broadcast system, in particular a DVB-T system.
16. Method as claimed in claim 1, wherein the moving entity is a
mobile phone, a navigation system, a computer, a PDA, a vehicle or
a piece of clothing.
17. Method as claimed in claim 1, comprising the steps of:
determining one or more channel frequency response signals based on
the received transmission signal by the antenna located foremost in
movement direction, storing said one or more channel frequency
response signals by at least one of the antennas, which are not
located foremost in a movement direction, determining a current
channel frequency response signal based on the received
transmission signal, and correlating the current channel frequency
response signal with at least one of the stored channel frequency
response signals to determine said time offset.
18. Device for determining the speed of a moving entity comprising:
at least two antennas for receiving a transmission signal, said
antennas being carried by said moving entity and displaced at a
predetermined distance, signal characteristics determination means
for determining signal characteristics from the transmission signal
as received by the at least two antennas, time offset determination
means for determining a time offset between the reception of the
transmission signal at the at least two antennas by comparing the
signal characteristics determined for the at least two antennas,
and speed determination means for determining the speed of the
moving entity from the determined time offset, the distance of the
at least two antennas and the direction of movement of the moving
entity relative to the arrangement of the at least two
antennas.
19. A device for determining the speed of the moving entity as
defined in claim 18, wherein the moving entity comprises a mobile
phone, navigation system, computer, a PDA, a vehicle or a piece of
clothing.
20. Computer program for determining the speed of a moving entity
carrying at least two antennas for receiving a transmission signal,
said antennas being displaced at a predetermined distance, said
computer program comprising program code means for causing a
computer to carry out the following steps when said computer
program is run on a computer: determining signal characteristics
from the transmission signal as received by the at least two
antennas, determining a time offset between the reception of the
transmission signal at the at least two antennas by comparing the
signal characteristics determined for the at least two antennas,
and determining the speed of the moving entity from the determined
time offset, the distance of the at least two antennas and the
direction of movement of the moving entity relative to the
arrangement of the at least two antennas.
Description
[0001] The present invention relates to a method and a
corresponding device for determining the speed of a moving entity,
such as a vehicle, a mobile phone or any other moving device, whose
speed might be of interest.
[0002] Known mobile speed estimation algorithms can be briefly
grouped into two categories: time-domain approaches, e.g. based on
the covariance approximations on the envelope level crossing rates
(LCR) or average fade duration, and frequency-domain approaches
based on the Doppler spectrum or on some parametric spectral
analysis. Such methods for speed estimation include estimating the
maximum Doppler frequency using spectrum methods, calculating the
squared deviations of the logarithmically compressed received
envelope, zero-crossing estimators, frequency domain time
covariance analysis and wavelet-based approaches. These methods
give generally good results at vehicular speeds (larger than 30
km/h). However, these algorithms are quite complicated and they do
not work satisfactorily at very low speeds (e.g. pedestrians speeds
up to 6 km/h).
[0003] JP 04-157388 A2 discloses a method and device for measuring
at least one of acceleration, speed and moving distance of a moving
body by determining a phase difference between standing waves of
the same frequency which are received simultaneously by two
antennas provided along the direction of movement of the moving
body. A standing wave of a half wavelength formed in a space by an
FM radio wave emitted from an FM station is received by the
antennas. The rear antenna (in the direction of movement) receives
the wave in a time delay compared to the front antenna. By
correlative processing the time delay is calculated wherefrom the
ground speed of the moving body is calculated.
[0004] It is an object of the present invention to provide a method
and device for determining the speed of a moving entity which is
much simpler than known methods, delivers accurate speed estimates
also for very slow moving entities, such as mobile terminals
carried by pedestrians, and which is not restricted to the use of a
standing FM radio wave emitted from an FM station.
[0005] The object is achieved according to the present invention by
a method as defined in claim 1 where the moving entity carries at
least two antennas for receiving a transmission signal, said
antenna is being displaced at a predetermined distance, said method
comprising the steps of: [0006] receiving a transmission signal by
the at least two antennas, [0007] determining signal
characteristics from the transmission signal as received by the at
least two antennas, [0008] determining a time offset between the
reception of the transmission signal at the at least two antennas
by comparing the signal characteristics determined for the at least
two antennas, and [0009] determining the speed of the moving entity
from the determined time offset, the distance of the at least two
antennas and the direction of movement of the moving entity
relative to the arrangement of the at least two antennas.
[0010] A corresponding device for determining the speed of a moving
entity according to the present invention is defined in claim 17.
The present invention relates further to a moving entity, in
particular a mobile phone, a navigation system, a computer, a PDA,
a vehicle or a piece of clothing, including a device for
determining the speed of the moving entity. Still further, the
present invention relates to a computer program comprising program
code means for causing a computer to carry out the steps of the
method according to the present invention when the computer program
is run on a computer. Preferred embodiments of the invention are
defined in the dependent claims.
[0011] The invention is based on the idea to measure a transmission
signal, which can generally be any signal transmitted over the air
and that is available for other purposes, such as transmission
signals of a wireless communication system or of a broadcast
system, by the at least two antennas and to determine signal
characteristics therefrom. Based on these signal characteristics,
which can, for instance, be the signal strength, signal phase or a
signal envelope, the transmission signals measured by the at least
two antennas are compared, and it is particularly determined with
which time offset the transmission signal is received by the at
least two antennas. Knowing this time offset and knowing the
predetermined distance at which the at least two antennas are
displaced, it is then possible to determine the speed of the moving
entity by a simple calculation, generally by a simple function
including only a division of said predetermined distance by the
determined time offset.
[0012] It shall be noted that according to the present invention it
is not mandatory that the transmission signal is received by at
least two antennas simultaneously or at the same time,
but--depending on the particular embodiment or application--the
transmission signal can also be received by different antennas
subsequently or at different moments in time.
[0013] It is generally known that due to reflection, refraction and
scattering effects, the strength of a signal received at a moving
receiver can vary considerably over time. As the receiver moves,
the strength and phase of incoming multipath radio waves change and
consequently different patterns of constructive and destructive
interference are formed at the receiving antenna. The interference
patterns will be relatively stable in space if the radio
propagation environment does not change significantly, i.e. if the
transmitter and scatterers in the immediate vicinity do not move.
The sum of the incoming signals gives rise to random fluctuations
in the received signal strength (RSS) on the order of a wavelength.
Hence, in a preferred embodiment envelope signals are determined
for the transmission signals received by the at least two antennas
which are then used as the signal characteristics. For instance,
the determined envelope signals are recorded and time stamped from
all antennas. Since the at least two antennas generally see
virtually identical RSS fading patterns but just offset in time,
the time offset will be proportional to the distance between the
antennas and inversely proportional to the speed of the moving
entity.
[0014] In particular when using envelope signals (or RSS
measurements) a simple pattern matching method is used in a
preferred embodiment giving an estimate of the temporal offset
between the measured and recorded patterns. This is preferably done
in real-time after each new transmission signal measurement is
made. In addition, preferably standard time warping techniques are
used to align the measurement patterns if large accelerations are
experienced. Odometry or cumulative distance estimates are then
given simply by numerical integration of the speed estimate.
[0015] In a preferred embodiment the moving entity carries two
antennas, preferably arranged along a line parallel to the
direction of regular movement of said moving entity, wherein for
each antenna signal characteristics are determined from the
received transmission signal and wherein the determined signal
characteristics are compared to determine said time offset. This is
the most simple embodiment for implementing the invention which,
however, only allows the determination of the speed of the moving
entity.
[0016] In another embodiment the moving entity carries a plurality
of antennas, preferably arranged in a common plane or a common
sphere, wherein for each antenna signal characteristics are
determined from the received transmission signal and wherein the
signal characteristics are compared to determine the time offsets
between the reception of the transmission signal at the plurality
of antennas. With this embodiment in addition to the speed also the
direction of movement can be determined from a comparison of the
time offsets at which the transmission signal is received by the
plurality of antennas. Furthermore, a plurality of antennas allows
a more accurate determination of the speed compared to the use of
only two displaced antennas.
[0017] Advantageously, the antennas of said plurality are arranged
at the corners of a polygon having equally long side lengths, at
equal distances along the circumference of a circle or equispaced
on the surface of a sphere, which simplifies the determination of
the time offsets and, if desired, the direction of movement.
[0018] The comparison of the signal characteristics can be
performed continuously so that all the time the current speed is
available. Alternatively said comparison can be performed from time
to time, for instance at regular intervals. In a preferred
embodiment thereof signal characteristics determined from a portion
of the signal as received by the antenna located foremost in
movement direction are stored as reference characteristics and
wherein the same signal characteristics are determined from the
transmission signals received by the other antennas and compared to
the reference characteristics to determine the time offset(s).
[0019] As mentioned above, signal characteristics of such a signal
portion could be the RSS fading pattern of an envelope signal so
that a simple pattern matching algorithm is used for comparison.
However, generally many different signal characteristics can be
exploited by the present invention, such as amplitude peak values,
energy values, delay, multipath delay characteristics, maximum
delay, root-means-square (RMS) of delay, maximum Doppler frequency,
Doppler spectrum or spectral characteristics of the transmission
signal.
[0020] In particular, when using an envelope signal as signal
characteristic, a portion of the envelope signal determined from
the transmission signal received by the antenna located foremost in
movement direction is stored as reference portion and the other
envelope signals are compared to this reference portion to
determine the time offset(s).
[0021] As mentioned above when using more than two antennas and
exploiting the transmission signals received by such a plurality of
antennas the direction of movement of the moving entity can be
determined. However, in an alternative embodiment the direction of
movement relative to the arrangement of the at least two antennas
is determined by use of direction measurement means, in particular
a navigation system or an acceleration sensor. The determined
direction can then be used to align the at least two antennas with
the direction of movement of the moving entity so that always or at
least just before the transmission signals for determining the
speed are received by the at least two antennas are essentially at
the same position with respect to the moving entity. For instance,
in the embodiment having only two antennas it can be foreseen that
the antennas are always on-the-fly aligned such that they are
positioned on a line parallel to the direction of movement, i.e. if
said direction changes also the position of the antennas is
corrected accordingly.
[0022] According to a further embodiment based on an RFSM (Radio
Frequency Signature Matching) based approach of the present
invention, which is easy to implement and which delivers reliable
speed estimates also for low velocities, comprises the steps of:
[0023] determining one or more channel frequency response signals
based on the received transmission signal by the antenna located
foremost in movement direction, [0024] storing said one or more
channel frequency response signals, [0025] determining a current
channel frequency response signal based on the received
transmission signal by at least one of the antennas, which are not
located foremost in movement direction, [0026] correlating the
current channel frequency response signal with at least one of the
stored channel frequency response signals to determine said time
offset.
[0027] Preferably for simplifying the calculations the at least two
antennas are identical.
[0028] Different transmission signals can be exploited by the
present invention, such as transmission signals of a wireless
communication system, in particular of a GSM, UMTS, WiMax, WiFi
system, or of a digital broadcast system, such as a DVB-T
system.
[0029] The invention will now be explained in more detail with
reference to the drawings in which
[0030] FIG. 1 shows an envelope signal of a received transmission
signal,
[0031] FIG. 2 shows a diagram illustrating one embodiment for use
of the invention,
[0032] FIG. 3 shows a schematic block diagram of a first embodiment
of a device according to the present invention using two
antennas,
[0033] FIG. 4 shows a schematic block diagram of an embodiment of a
device according to the present invention using a plurality of
antennas,
[0034] FIG. 5 shows schematic block diagram of a further embodiment
of a device according to the present invention using a plurality of
antennas,
[0035] FIG. 6 shows a diagram illustrating the positions of three
antennas at different times,
[0036] FIG. 7 shows a top view on the arrangement of a plurality of
antennas according to different embodiments,
[0037] FIG. 8 shows a diagram illustrating a further embodiment for
use of the invention,
[0038] FIG. 9 the time-domain envelope of a typical OFDM
symbol,
[0039] FIG. 10 shows the tones in an OFDM symbol with 19
subcarriers,
[0040] FIG. 11 shows an example of an input/output relationship for
a channel,
[0041] FIG. 12 illustrates one dimensional speed estimation using
RF signature matching,
[0042] FIG. 13 shows a block diagram of the channel frequency
response buffers required for the embodiment illustrated in FIG.
12,
[0043] FIG. 14 shows a flow chart of a method according to the
embodiment illustrated in FIG. 12, and
[0044] FIG. 15 shows a block diagram of the device according to the
embodiment illustrated in FIG. 12.
[0045] FIG. 1 shows a typical envelope signal of a transmission
signal received by an antenna adapted for reception of said
transmission signal. The strength and phase of incoming multipath
radio waves change due to reflection, refraction and scattering
effects and due to movement of the receiving antenna so that
different patterns of constructive and destructive interference are
formed there. However, if the radio propagation environment does
not change significantly, at least in a certain time interval, the
interference patterns will be relatively stable. The sum of signals
received by an antenna under these assumptions, called envelope
signal, thus shows random fluctuations in the received signal
strength and fast fading effects as shown in the example of FIG. 1.
If a constant speed of the antenna is assumed, the horizontal axis
(time axis) can be converted to a spatial position.
[0046] A simple configuration of an example for use of the
invention is illustrated in FIG. 2. In this example the speed of
the vehicle 1 shall be determined, said vehicle moving at present
to the right-hand side in forward direction as indicated by arrow
2. At a known distance apart and aligned with the direction 2 of
motion of the vehicle 1, such as a car, two antennas are fixed,
i.e. a front antenna 3 and a rear antenna 4, on the roof. As the
vehicle 1 moves the transmission signal 10 is received, recorded
and time stamped from both the front and rear antennas 3, 4, for
instance in finite length first-in first-out FIFO buffers. The two
antennas 3, 4 will see virtually identical RSS fading patterns, but
just offset in time due to the displacement of the two antennas 3,
4 in movement direction 2. This offset in time is determined by
comparing the two measured and recorded RSS measurements. In
particular, signal characteristics of the received transmission
signal 10 are determined, such as an envelope signal, or at least
characterising portions of such envelope signal, and these signal
characteristics are used to correlate the transmission signals
received by the two antennas 3, 4. Therefrom the time offset is
calculated from which the speed of the vehicle 1 can be easily
determined, as the speed is proportional to the (known) distance
between the antennas 3, 4 and inversely proportional to the
calculated time offset.
[0047] A schematic block diagram of a device for determining the
speed of the vehicle 1 using two antennas as illustrated in FIG. 2
is shown in FIG. 3. As shown there for each of the antennas 3, 4 a
signal characteristics determination unit 31, 41 is provided which
are essentially identical and determine the same type of signal
characteristics from the transmission signal provided from the
respective antenna 3, 4. In a comparison unit 6 the determined
signal characteristics are compared. In particular, a pattern
matching technique is applied there in order to find matching
patterns determined as signal characteristics from the transmission
signals. If matching patterns have been found the time offset
between the reception of the transmission signals by the two
antennas 3, 4 is determined in a time offset determination unit 7
which is generally possible in a simple way since the signal
characteristics are time stamped or at least a time index is
available for the signal characteristics. Once the time offset is
known the speed can be easily determined in a speed determination
unit 8 as described above.
[0048] According to this embodiment the speed determination can be
improved by using the multiple sub-carriers that are found in
certain wireless communication systems, such as DVB-T, 802.11b/b,
WiMax. In this case the speed determination derived from all the
sub-carriers can be averaged for greater accuracy. According to
another embodiment, which will be explained below, the wide
bandwidth (displaying frequency selective fading) provides higher
resolution frequency response data which is better for signature
matching.
[0049] The method according to the present invention is far simpler
than speed estimates based on single-antenna RSS measurements. It
requires only simple memory means, such as FIFO-buffers (which are,
for instance, included in the signal characteristics determination
units 31, 41 of the embodiment shown in FIG. 3) and very simple
comparisons such as pattern alignment algorithms.
[0050] Speed estimates for pedestrian speeds (indoor and outdoor)
are thus possible and give better results than known methods.
Compared to the method known from JP04-157388 A2 the present
invention is not restricted to the use of standing FM radio waves
emitted from an FM station, said FM radio waves having a fixed
frequency and wavelength to which the distance of the antennas in
the known embodiment must be adopted. The method according to the
present invention can be used with many different types of
transmission signals and allows to freely set the distance of the
antennas which is particularly important if the moving entity is
much smaller than a vehicle so that the distance can not be adopted
to the wavelength of an FM radio wave as is required by the known
method. Furthermore no standing waves having a fixed frequency
wavelength are required, in particular if pattern matching
techniques are used for comparison of these signal characteristics.
Hence, different applications are possible with the present
invention, in particular the use of more than two antennas which
allow not only the determination of the speed but also of the
direction of movement as will be explained below.
[0051] FIG. 4 shows a schematic block diagram of an embodiment of a
device according to the present invention using a plurality (i.e.
more than two) of antennas. Just as an example three antennas 3, 4,
5 are shown; in practise, more than three, such as four, eight or
sixteen antennas can be easily used. For each transmission signal
as received from the antennas 3, 4, 5 signal characteristics are
determined in determination units 31, 41, 51. In this embodiment
the signal characteristics as determined from the transmission
signal of antenna 4 are stored in a storage unit 9 to which in
comparison units 61, 62 the signal characteristics determined from
the transmission signals received from antennas 3 and 5 are
compared. The comparison result is provided to a time offset
determination unit 7 which, for each of antennas 3, 5, determines
the time offset of reception of the respective trans-mission signal
with respect to the reception of the transmission signal by antenna
4. From these two time offsets and the known distances between the
antennas 3, 4, 5 the speed of the moving entity is determined in
unit 8.
[0052] Different methods exist to estimate the speed when using
more than two antennas. According to an embodiment as shown in FIG.
5, which is similar to the embodiment shown in FIG. 4 but comprises
a storage unit 91, 92, 93 for every antenna 31, 41, 51 and only one
comparison unit 6, every antenna captures the signal at the same
time and stores it for a time t.sub.max=d.sub.x,y/v.sub.max (e.g.
in seconds), where d.sub.x,y is the distance between antenna x and
antenna y (e.g. in meters), and v.sub.max is the maximum possible
speed (e.g. in m/s). The time t.sub.max is also the processing
window. The channel is sampled every t.sub.s seconds which is much
shorter than the processing window. Within the processing window
the channel and characteristics thereof of each antenna are
matched/compared to/with the respective channel characteristics of
the other antenna(s). The time (in number of samples from the
beginning of the processing window) is captured when an optimum
match between the stored signal (taken at time t.sub.0) and the
currently sampled signal (taken at time t.sub.0+t.sub.s) between
any two antennas is achieved. This is illustrated in FIG. 6 which
schematically shows the positions of the three antennas 1, 2, 3 at
time t.sub.0 and at time t.sub.0+t.sub.s (indicated by reference
signs 1', 2', 3') and where for each antenna at the two shown
positions an example for captured antenna specific channel
characteristic numbers at the beginning of the processing window is
given.
[0053] The speed can be estimated as follows: The antennas are
assumed to be equidistantly spaced at d.sub.x,y (which is however
not a stringent requirement). The speed can then be estimated by
dividing d.sub.x,y by the average of the time differences for
optimum match including all possible antenna pairs. Alternatively,
instead of taking the average, the time difference which gives the
highest level of similarity between any two signals could be
used.
[0054] In addition to the speed also the direction of movement can
be easily determined. Since generally not only the distances
between the antennas and the time offsets of reception of the
transmission signals are known, but also the relative position of
the antennas to each other is known, it is possible by a simple
calculation to determine the direction in which a moving entity
moves.
[0055] The preferred method for estimating the direction of
movement is similar to the speed estimation assuming multiple
antennas. In this case the method of "highest level of similarity"
is used. The actual direction of movement is along the straight
line of those antenna pair which in the signal processing as
described above resulted in the highest level of similarity. With
this method differential heading information can be obtained. If an
absolute reference, e.g. the direction of the magnetic north is
available, absolute heading information can be obtained.
[0056] Although generally the antennas of a plurality of antennas
can be freely positioned it is preferred for each of the
calculations to be made that the antennas are located at equal
distances and in one plane. Top views of different embodiments for
pluralities of antennas are shown in FIGS. 7a and 7b according to
which in one embodiment (FIG. 7a) four antennas are located at the
corners of a square or where according to another embodiment eight
antennas are positioned along the circumference of a circle (FIG.
7b). Moreover, the antennas can also be positioned along the
surface of a three-dimensional body, such as a sphere or a cube so
as to allow speed and movement direction measurements in three
dimensions.
[0057] In particular in embodiments using only two antennas it is
preferred that the antennas are aligned in the direction of
movement in order to accurately determine the time offset. When
using the invention for a speed determination of a vehicle this is
easily possible since there the antennas can be fixed to the
vehicle accordingly, since a vehicle, such as a car, has only two
major directions of movement.
[0058] However, for other applications, for instance when the speed
of movement of a mobile unit, such as a PDA, computer, handheld
navigation system or mobile phone, shall be determined, all
possible directions of movement are available. If the antennas are
aligned in the direction of movement thus depends on the way in
which the user holds the device. The same holds for other
applications, where, for instance, antennas are fixed to a body or
piece of clothing or other equipment, where the body can also move
laterally. Hence, in a further embodiment of the present invention,
such as shown in FIG. 8, a direction measurement unit 21, such as a
navigation system or an acceleration sensor, is provided in the
moving entity 20, which can be here a mobile telephone. By this
unit 21 the current direction of movement of the device 20 can be
determined continuously, or at least at desired points in time when
a speed measurement shall be made. The antennas 23 and 24 are in
this embodiment placed on a movable alignment unit 22 (elements 22
to 24 can also be located inside the device 20), such as a
rotatable plate, which can be rotated by a motor 25 under control
of the direction measurement unit 21. When the device 20 changes
the direction of movement, that change will be recognized by the
direction measurement unit 21 which then controls the motor 25 to
rotate the alignment plate 22 such that the antennas 23 and 24 are
aligned with the changed direction of movement 2 so that the
antennas 23 and 24 are positioned as shown in FIG. 8.
[0059] In the following a further embodiment of the present
invention, in particular using Radio Frequency Signature Matching
(RFSM), will be illustrated. Before the details of said embodiment
will be explained, some general comments shall be provided as to
the principles of Orthogonal Frequency Division Multiplex (OFDM)
which can be employed according to the present invention. It shall,
however, be noted that any kind of wireless system can be used
according to the invention (not necessarily an OFDM system) that is
capable of displaying frequency selective fading.
[0060] DVB-T is a wireless system that utilizes OFDM as the digital
modulation scheme. The following figures refer to OFDM in general.
FIG. 9 shows the time-domain envelope of a typical OFDM symbol. The
symbol duration is depicted as 224 .mu.s--a value common to DVB-T
systems operating in the 2K mode. It is clear from the envelope
that it must contain a large number of frequency tones. The example
is in fact composed of about 1705 "tones" or "subcarriers" (the
common nomenclature)--each modulated by the user data. An example
is illustrated in FIG. 10.
[0061] As is clear from the example, each subcarrier is orthogonal
to the others and hence there is no interference as long as the
system is perfectly frequency synchronized. In a DVB-T system
operating in the 2K mode, there are 1705 subcarriers. 45 of those
are used as "pilots". These are known subcarriers that are
distributed over the entire frequency band and can be used for
channel estimation purposes.
[0062] A channel can be thought of as a filter with a certain
frequency response characteristic of the environment. Therefore, as
with any filter, the "frequency profile" of the output can be
obtained by multiplying the "frequency profile" of the input by the
channel frequency response. This is illustrated in FIG. 11. As a
direct consequence, if some parts of the input are known (pilots),
the channel frequency response can be easily estimated from the
received signal. This procedure is commonly known as channel
estimation. For the proposed embodiment of the speed estimation
algorithm explained in the following, this estimate of the channel
frequency response is required. It is not important how this is
obtained--there exists multiple methods for reliable channel
estimation, which are well known to the skilled person, so that it
is assumed that it is a quantity that is readily available.
[0063] Channel estimation is a very widely studied and well
understood topic. Such channel estimation methods are, for
instance, described in detail in "OFDM channel estimation with
timing offset for satellite plus terrestrial multipath channels";
Yeon-Su Kang; Do-Seob Ahn; Ho-Jin Lee; Vehicular Technology
Conference, 2006. VTC 2006-Spring. IEEE 63rd, Volume 6, 2006
Page(s):2592-2596 and "Efficient Implementation of Robust OFDM
Channel Estimation"; Auer, G.; Personal, Indoor and Mobile Radio
Communications, 2005; IMRC 2005. IEEE 16th International Symposium
on Volume 1, 11-14 Sep. 2005 page(s):629-633. Equalization is a
topic that is closely related to channel estimation and is treated
in all communications textbooks. One popular example is "Digital
Communications"; John G. Proakis; 4.sup.th Edition; McGRAW-HILL
INTERNATIONAL EDITION Electrical Engineering Series.
[0064] Now, the proposed embodiment shall be explained in more
detail.
[0065] Due to its very nature, the Global Positioning System (GPS)
does not work well (if at all) indoors or in other heavily shadowed
locations. It requires a direct line of sight (LOS) between the
user equipment (UE) and the GPS satellites for accurate
geolocationing--a requirement that cannot be fulfilled in the
aforementioned locations. This limitation has motivated research
into indoor locationing methods, one of which is the PDR
(Pedestrian Dead Reckoning) approach. The general idea behind PDR
is to start at a location with known coordinates and guess the
subsequent locationing data from an estimate of the user's speed
and heading. Evidently, an accurate estimate of the user speed is
vital for PDR based locationing schemes.
[0066] A typical approach is via an estimation of the user's step
length, as for instance described in Jani Kappi, Jari Syrjarinne,
and Jukka Saarinen, MEMS-IMU based-pedestrian navigator for
handheld devices, in ION GPS 2001, pages 1369-1373, Salt Lake City,
Utah, Sep. 11-14 2001 or Helena Leppakoski, Jani Kappi, Jari
Syrjarinne, and Jarmo Takala, Error analysis of step length
estimation in pedestrian dead reckoning, in ION GPS 2002, pages
1136-1142, Portland, Oreg., Sep. 24-27 2002. First, an
time-acceleration magnitude signal is obtained from the three
orthogonal accelerometers. Steps are then defined by the
positive-going zero crossings of the low-pass filtered version of
that signal. Next, numerical parameters describing the step model
(maximum/minimum acceleration, time between steps) are calculated.
These parameters are then used in a feedforward neural network (NN)
as input training patterns. The output training patterns are the
step lengths estimated from GPS position fixes, interpolated to
footfall occurrences. The NN can then be optimized using non-linear
optimization techniques such as the scaled conjugate gradient
method.
[0067] The other academically popular class of estimators are based
on the level crossing rate (LCR) approach. The LCR of a random
process can yield a lot of useful information about the underlying
process. Used in conjunction with the Rayleigh fading envelope seen
by mobile terminals (MT), estimates of the maximum Doppler
frequency and hence the speed can be obtained. The mobile speed
estimation (MSE) technique proposed by Zhao and Mark (Lian Zhao and
Jon Mark, Mobile speed estimation based on average fade slope
duration, in IEEE Transactions on Communications, Vol. 52, No. 12,
pages 2066-2069, December 2004) makes use of the zero crossing rate
(ZCR) of the slope (first derivative) of the underlying fading
process to obtain an estimate of the maximum Doppler frequency. The
average number of sampling intervals in a positive-going (or
negative-going) slope of the fading envelope defines the average
fade slope duration (AFSD) and is directly related to the maximum
Doppler frequency. From an estimate of the AFSD, the speed can be
calculated in a straight-forward manner. Although the algorithm is
quite accurate for high speeds, its accuracy drops at the low
speeds associated with pedestrians. Narasimhan and Cox (Ravi
Narasimhan and Donald Cox, Speed estimation in wireless systems
using wavelets, in IEEE Transactions on Communications, Vol. 47,
No. 9, pages 1357-1364, September 1999) proposed another MSE
algorithm that makes use of the fact that the number of local
minima of the fading envelope (in a semilog sense) over a
wavelength is directly related to the mobile speed. Therefore, from
an estimate of the mean distance between the local minima of the
fading envelope, the speed can be calculated. Continuous wavelet
transform is used to determine the separation. Although the
simulation results shows a good level of accuracy at low speeds,
the need for the CWT makes this method computationally complex and
expensive.
[0068] The solution proposed according to the present embodiment
here can be classified as a RFSM based approach. However, unlike
traditional RFSM locationing algorithms that must have prior
knowledge of the RF signature at known coordinates, the proposed
embodiment requires no such a priori information. The proposed
method requires a multi-antenna setup and a functional RF source
such as a DVB-T transmitter station. For each spatial dimension, at
least two antennas are required.
[0069] For simplicity, let movement be confined to one dimension
only (the idea applies to the general 3-dimensional case in a
straight-forward manner). Then, assume that the plane of the
antenna array comprising the two antennas 3, 4 is parallel to the
direction of motion 2 of the moving entity 1 as shown in FIG. 12.
Now, if the array moves forward with a speed v then the channel
seen by antenna 3 at time t=0 will also be seen (slightly
evolved--depending on speed) by antenna 4 at time t=.tau.. As the
distance d between the antennas is predefined and therefore known,
the speed is easily estimated using the formula v=d/.tau..
[0070] The estimate of the channel frequency response (CFR) may be
used as the RF signature metric. By correlating the current CFR
estimate from antenna 4 with a number of previous CFR estimates
from antenna 3, the time delay can be determined. The number of
previous CFR estimates maintained will dictate the accuracy of the
estimation algorithm.
[0071] As such, the method requires a certain amount of memory to
hold the CFR estimate database but is computationally rather cheap.
This is in contrast to the wavelet based LCR method that requires
CWT at each stage. Although the AFSD method is computationally
simple and requires little memory, it is only accurate at high
speeds--a scenario that does not quite relate to the problem of
pedestrian locationing & navigation. Finally, the neural
network used for the step length estimation based method requires
extensive prior training and therefore makes the device highly
personalized. Also, it cannot cope with sudden changes in walking
behaviour. These problems are irrelevant for the proposed method
here, as it requires no training and is able to adapt to changes in
the user's speed.
[0072] As stated earlier, the proposed method requires a database
associated with antenna 3 that is capable of storing the current
and N previous CFR estimates. The CFR estimate buffer B associated
with antenna 4 only needs to hold the current estimate (FIG.
13).
[0073] At every estimation instant, the current CFR estimate at
antenna 4 is crosscorrelated against the CFR estimates in database
A associated with antenna 3 to determine the lag. The detection
process is therefore
lag = ar g max k ( B * A k ) ; ##EQU00001## k = 0 , , N .
##EQU00001.2##
The operator "*" represents the cross-correlation at delay=0. If
the lag is detected to be zero, it is simply ignored and the
algorithms moves on to the next speed estimation cycle.
[0074] As the cross-correlation at delay=0 is nothing more than the
dot-product between the two vectors B and A.sub.k, the entire
procedure simplifies to a series of normalized dot-product
calculations followed by a maximum detection. The simplified
detection rule is then
lag = arg max k ( B A k B A k ) ; ##EQU00002## k = 1 , , N .
##EQU00002.2##
In order to define further system parameters, certain physical
constraints must be defined. Assuming that the average walking
speed of an adult human is between 1-1.5 m/s, lower and upper speed
detection limits of v.sub.min=0.1 m/s and v.sub.max=15 m/s
respectively are proposed. With an antenna separation distance d,
the shortest delay detectable (i.e. a lag of 1) will be d/15. The
longest delay that needs to be accommodated is then given by d/0.1.
Therefore the minimum required size for the database is
[ ( d 0.1 ) ( d 15 ) ] + 1 = 151. ##EQU00003##
i.e. N=150. The one additional slot is needed to store the current
CFR estimate.
[0075] At this point, the CFR estimation frequency must be defined.
Assuming a DVB-T radio source with 8 MHz channels and operating in
2K mode, the duration of one OFDM symbol, T.sub.s, is 224 .mu.s. As
there are 1705 subcarriers, each CFR estimate consists of 1705
samples. The estimation interval, (in terms of the number of OFDM
symbols that must elapse between estimates) is defined as
i ^ = d v max .times. T s .times. 1 .mu. . ##EQU00004##
where .mu. is an adaptive scaling parameter used to improve the
accuracy of the estimates. .mu. has a range of [1, (.mu.=1)].
Starting with an initial value of 1, the rough speed estimates are
used to find the new value of .mu. to be used via linear
interpolation. To prevent false measurements, the
correlation-product with the highest value is compared against a
predefined threshold.
[0076] Higher speed results in a higher value of .mu. resulting in
an increase in the estimation frequency. Due to the increased
frequency, shorter time lapses can be detected and hence higher
speeds can be accurately estimated. Please see the following
example:
let .mu.=1, d=0.05 m, V.sub.max=15 m/s, T.sub.s=224 .mu.s. Then:
=14.fwdarw.i.e. estimation is done every 14 OFDM symbols==3.136
ms.
[0077] So the speeds that can be detected are:
Lag=1: 0.05/(0.003136*1)=15.944 m/s
Lag=2 0.05/(0.003136*2)=7.9719 m/s
Lag=3 0.05/(0.003136*3)=5.3146 m/s
Lag=4 0.05/(0.003136*4)=3.9860 m/s
. . .
Lag=148 0.05/(0.003136*148)=0.10773 m/s
Lag=149 0.05/(0.003136*149)=0.10701 m/s
Lag=150 0.05/(0.003136*150)=0.10629 m/s
[0078] Now let .mu.=14, d=0.05 m, V.sub.max=15 m/s, T.sub.s=224
.mu.s. Then:
=1.fwdarw.i.e. estimation is done every OFDM symbols==224
.mu.s.
[0079] So the speeds that can be detected are:
Lag=1: 0.05/(224 .mu.s*1)=223.21 m/s
. . .
Lag=14 0.05/(224 .mu.s*14)=15.944 m/s
Lag=15 0.05/(224 .mu.s*15)=14.881 m/s
Lag=16 0.05/(224 .mu.s*16)=13.951 m/s
Lag=17 0.05/(224 .mu.s*17)=13.13 m/s
. . .
Lag=148 0.05/(224 .mu.s*148)=1.5082 m/s
Lag=149 0.05/(224 .mu.s*149)=1.4981 m/s
Lag=150 0.05/(224 .mu.s*150)=1.4881 m/s
[0080] As seen from the examples, a high value of mu improves the
accuracy at higher speeds while sacrificing the ability to detect
lower speeds. A low value of .mu. allows accurate detection of low
speeds at the expense of accuracy at high speeds. This adaptive
behaviour is one of the key features of our proposal.
[0081] To prevent false measurements, the correlation-product with
the highest value is compared against a predefined threshold. FIG.
14 shows an algorithmic flowchart of the one-dimensional and
unidirectional speed estimation algorithm. Therein CRE means
channel response estimate, "dbA[ ]" is the CRE buffer A associated
with antenna 3. The function xcorr(a, b, L) provides the normalized
cross correlation product at lag=L, i.e. it performs the operations
necessary to compute the parenthesized expression of the above
formula for determining the lag.
[0082] A block diagram of the device of the last described
embodiment is shown in FIG. 15. There is shown a first antenna
array 50 provided--in this embodiment--with two antennas arranged
along a first direction (here x-direction) and respective tuners
50a, 50b for said antennas. Further, a second antenna array 60
provided--in this embodiment--with two antennas arranged along a
second direction (here y-direction) and respective tuners 60a, 60b
for said antennas is shown. The outputs of the tuners 50a, 50b and
60a, 60b are connected to the I.sup.2C bus 70 to which also an FPGA
platform 80 is connected via an I.sup.2C controller 82. Said FPGA
platform 80 further comprises an FPGA 81, in which the main steps
of the above described embodiment are carried out, including a
correlation means 81a, an argmax and threshold calculation means
81b and a speed calculation means 81c for speed calculation and
adaptation of the estimation frequency through a change of the
parameter .mu. (determined by linear interpolation from the current
speed estimate). Said FPGA is further connected to a storage means
83, e.g. an SDRAM or a flash memory. For displaying the calculated
result a speed display 84 may be provided.
[0083] According to the described embodiment it is assumed that the
antenna array remains fixed to the horizontal plane--i.e. the array
is not free to rotate in the vertical plane. The purpose of the
device is to provide a speed estimation--pedestrian speed
estimation is simply an example of possible applications.
[0084] An analysis of the algorithm's complexity reveals that any
recent high-density FPGA based platform is therefore suitable for
this purpose. To store the required data, about 2 Megabytes of
SDRAM (fast-memory) is sufficient. 4 DVB-T tuner boards f can be
used to obtain the required channel frequency response over the
I.sup.2C bus--implying that a I.sup.2C controller is required on
the hardware platform.
[0085] If the above described embodiment is to be extended to the
three-dimensional and bidirectional case, an array with antenna
pairs in each of the three dimensions will be required. For
bidirectional support, databases for the channel response estimates
are maintained for each antenna in a pair. Then, six independent
estimation processes (two for each antenna pair) are run in
parallel. The non-zero speed estimates from each pair can then be
added as a vector sum to yield the velocity of the device in
three-dimensions.
[0086] The described embodiment can be easily implemented in
relatively inexpensive hardware. It can provide reliable estimates
at low velocities--a feature that is uncommon for most existing
methods.
[0087] The proposed embodiment finds direct application in the
field of indoor mobile positioning using Pedestrian Dead Reckoning
(PDR). However, it can also be readily utilized to solve any
problem that requires speed estimation without the use of
conventional equipment. Applications of this embodiment may
particularly include the integration in hand-held devices such as
PDAs/cellphones for pedestrian speed estimation and stand-alone
devices for measuring human running/walking speed and the speed of
transports such as bicycles, kick scooters, skateboards,
rollerblades, etc.
[0088] It should be mentioned that multiple antennas can also be
used to solve the problem of movement in any direction.
[0089] The present invention can be used in many different
applications. One application is to accurately and continuously
measure the speed of a vehicle, such as a car. In particular under
conditions, such as snow or heavy rain, where the wheels might get
locked due to heavy breaking so that the speed can not be
determined via the wheels, the invention can be used.
[0090] Further applications are relative positioning of pedestrians
(dead reckoning), locating children, animals and goods/containers,
tracking of cars and other objects, aiding tools for
handicapped/blind people.
[0091] Multiple antenna receivers are becoming more prevalent so
there could be very little extra cost to the available system. If
MIMO systems become more prevalent in the automotive sector (for
example for mobile DVB-T or WiMax reception), then the speed
estimate could be made available to the vehicle (CAN) bus which
would then an interesting addition to the ABS sensor based speed
estimates.
[0092] It follows a further description of the invention:
1 INTRODUCTION
[0093] Pedestrian dead reckoning (PDR) is a popular choice for
positioning and navigation in areas (e.g. indoors) where GPS based
solutions cannot be used. Two pieces of information are essential
before a valid location estimation can be made: a reference point
with known coordinates and the velocity (speed and heading) at
sufficiently close and successive intervals. Given the needed
information, displacement of the user from the reference location
can be approximated and hence an estimate of the new location
coordinates can be obtained.
[0094] Accurate heading information is readily available from
sensors such as a ring laser gyro [1]; it is the speed estimate
that has been difficult to obtain with a sufficient degree of
accuracy. Common approaches seen in literature include the use of
step length estimates [2, 3] and the use of various algorithms that
aim at extracting speed information from the Rayleigh fading
envelope seen at a radio receiver terminal attached to the user [4,
5, 6]. However, each method suffers from certain drawbacks.
[0095] Step length estimates can be obtained from neural networks
trained with the user's walking pattern. The first step is to start
with a time-acceleration magnitude signal obtained from three
orthogonal accelerometers. Steps are then defined by the
positive-going zero crossings of the low-pass filtered version of
that signal. Next, numerical parameters describing the step model
(maximum/minimum acceleration, time between steps) are calculated.
These parameters are then used in a feed-forward neural network
(NN) [7] as input training patterns. The output training patterns
are the step lengths estimated from GPS position fixes,
interpolated to footfall occurrences. The NN can then be optimized
using non-linear optimization techniques. As the method requires
clear detection of the user's footfalls, any physical device can
only be used by pedestrians and must be mounted on the person. The
other problem is that errors tend to accumulate with every step--at
the absence of frequent absolute coordinate updates from an
external source, the error quickly becomes unacceptable. It is also
unable to cope with sudden changes in the user's walking
pattern.
[0096] The level crossing rate (LCR) of a random process can yield
a lot of useful information about the underlying process. Used in
conjunction with the Rayleigh fading envelope seen by mobile
terminals (MT), estimates of the maximum Doppler frequency and
hence the speed can be obtained (3). The mobile speed estimation
(MSE) technique proposed by Zhao and Mark [5] makes use of the zero
crossing rate (ZCR) of the slope (first derivative) of the
underlying fading process to obtain an estimate of the maximum
Doppler frequency. The average number of sampling intervals in a
positive-going (or negative-going) slope of the fading envelope
defines the average fade slope duration (AFSD) and is directly
related to the maximum Doppler frequency. From an estimate of the
AFSD, the speed can be calculated in a straight-forward manner.
Although the algorithm is quite accurate for high speeds, its
accuracy drops at the low speeds associated with pedestrians.
Narasimhan and Cox [6] proposed another MSE algorithm that makes
use of the fact that the number of local minima of the fading
envelope (in a semilog sense) over a wavelength is directly related
to the mobile speed. Therefore, from an estimate of the mean
distance between the local minima of the fading envelope, the speed
can be calculated. Continuous wavelet transform (CWT) is used to
determine the separation. Although the simulation results shows a
good level of accuracy at low speeds, the need for the CWT makes
this method computationally complex and expensive.
[0097] In this paper, a novel method of speed estimation using
relative radio frequency (RF) signature matching is described. The
proposed method correlates the RF signatures at two antennae
separated by a known distance to determine the time it takes for
the trailing antenna to "see" the same channel conditions as that
seen by the leading antenna. As the antenna separation is
predefined and known (e.g. in a MIMO device), the speed is easily
calculated from an estimate of the time delay. As will be shown
later, it also employs an adaptive algorithm that allows accurate
estimates at both high and low velocities. Furthermore, it is
computationally extremely simple as the main operation only
involves the calculation of correlation values between two channel
estimates.
[0098] The rest of this paper is organized as follows. In Section 2
the wireless channel model considered is presented and in Section 3
the speed estimation algorithm is described. Section 4 provides
details of a MATLAB realization and the simulation results. Section
5 concludes the paper.
2 WIRELESS CHANNEL MODEL
[0099] In a typical urban environment (specially indoors), there is
hardly a single dominant line of sight (LOS) path between a radio
transmitter and receiver. Signals can experience multiple
reflections, refraction, diffraction, and scattering before
reaching the receiver. As a result, the received signal consists of
multiple delayed versions of the original signal that arrive with
random delays, phase-shifts, and angle of arrival (AoA). This is
known as multipath effect. In addition to the above, the signal can
also experience Doppler shifts caused by movements of the
transmitter, receiver, and/or any objects affecting the channel.
The signal received from a multipath channel by an antenna array
can be represented by
h .fwdarw. ( t , .tau. ) = l = 0 L ( t ) - 1 A l ( t ) exp j .phi.
l ( t ) a .fwdarw. ( .theta. l ( t ) ) .delta. ( t - .tau. l ( t )
) ( 1 ) ##EQU00005##
where L (t) is the number of multipath components, A.sub.l is the
amplitude, .phi..sub.l is the carrier phase shift, .tau..sub.l is
the time delay, and .theta..sub.l is the AoA of component l. The
amplitude is modeled as a Rayleigh distributed random variable and
the AoA and phase shift are uniformly distributed [8]. Furthermore,
the delay is exponentially distributed [9]. {right arrow over
(a)}(.theta..sub.l(t)) is known as the array response vector. When
the signal and antenna array (containing m antennae) are restricted
to a two-dimensional space, the array response vector is given
by
a .fwdarw. ( .theta. l ( t ) ) = [ exp ( - j .PSI. l , 1 ) exp ( -
j .PSI. l , 2 ) exp ( - j .PSI. l , 3 ) exp ( - j .PSI. l , m ) ] (
2 ) ##EQU00006##
where .PSI..sub.l,i(t)=[x.sub.i cos(.theta..sub.l(t))+y.sub.i
sin(.theta..sub.l(t))].beta. and
.beta. = 2 .pi. .lamda. ##EQU00007##
is the wave-number [8].
[0100] The maximum Doppler shift, f.sub.d(max), experienced by a
receiver is dependent on the speed and is given by
f d ( max ) = v .lamda. ( 3 ) ##EQU00008##
where .nu. is the speed and .lamda. is the carrier wavelength [10].
The presence of Doppler spread in a multipath channel causes it to
display variations in time (small scale fading): the higher the
Doppler frequency, the shorter is the coherence time [10]
T c = 0.423 f d ( max ) ( 4 ) ##EQU00009##
of the channel. Fortunately, at velocities typical of pedestrians
(approx. 1.5 ms.sup.-1), the resultant Doppler frequency is small
(1.6 Hz for a carrier frequency of 474 MHz) and hence the coherence
time of the channel is quite large (approximately 0.26 s). FIG. 16
shows the space-time characteristics of a multipath Rayleigh fading
channel with a low Doppler spread.
[0101] It is clear from the plot that although the channel stays
nearly constant over time, it shows rapid variations in space--the
"coherence distance" is on the order of some tens of centimeters.
The behavior displayed by the channel, in terms of coherence time
& space, is exactly as needed for the proposed speed estimation
technique.
3 RF SIGNATURE MATCHING ALGORITHM
[0102] As stated in the previous section, a channel response that
shows rapid variations in space but remains relatively unchanged in
time is essential to the success of the speed estimation algorithm.
It requires a multi-antenna setup and an accessible RF source such
as a local digital television transmitter. For each spatial
dimension, at least two antennae are required. For simplicity, let
movement be confined to a single spatial dimension only (the idea
applies to the general three dimensional case in a straightforward
manner) and let the antenna array be aligned parallel to the
direction of motion as shown in FIG. 17.
[0103] At time t=0 s, the antennae are as shown by the markers in
FIG. 16. As the array moves forward, it traces a diagonal line in
the space-time plane and after some t seconds, the trailing antenna
(Antenna B) will be at the very same point in space that the
leading antenna (Antenna A) occupied t seconds ago. As the
coherence time of the channel is quite large, Antenna B should see
a channel response that is very similar to that seen by Antenna A t
seconds ago--i.e. the two responses would be highly correlated.
Therefore, the time delay can be estimated by correlating the
channel responses seen by Antenna B with a number of previous
channel responses seen by Antenna A. As the antenna separation is
predefined and known, the speed is easily calculated using
.upsilon. = d t ( 5 ) ##EQU00010##
once the time delay has been determined. At this point it should be
clear that a knowledge of the fading process or even an accurate
estimate of the channel is not necessary for this technique. As the
algorithm compares the relative match between two channel
estimates, any error or uncertainty in the estimates can be ignored
as long as both estimates are affected similarly by the error.
[0104] Obviously, a database of channel responses for Antenna A
must be maintained. The size of the database will dictate the
accuracy of the estimation algorithm. For the simple case where the
movement is confined to a single spatial dimension and in the
forward direction only, a database capable of holding N previous
and the current channel response estimate (CRE) from Antenna A will
be required. The buffer associated with Antenna B only needs to
hold the instantaneous CRE as depicted in FIG. 18. In addition to
the CRE database, a database of associated timestamps will also
have to be kept.
[0105] At every instant of the speed estimation process, the
current CRE at Antenna B is cross-correlated against the CREs at
Antenna A to determine lag. Therefore, the detection process
is:
lag = argmax k ( B * A k ) ; k = 1 , , N . ( 6 ) ##EQU00011##
[0106] The operator ".star-solid." represents the cross-correlation
at delay=0. The cross-correlation product B.star-solid.A.sub.0 is
not considered since it might result in the lag being detected as
zero--implying infinite speed.
[0107] As the cross-correlation at delay=0 is nothing more than the
dot-product between the two vectors B and A.sub.k, the entire
procedure simplifies to a series of normalized dot-product
calculations followed by a maximum detection. The simplified
detection rule is then
lag = argmax k ( B A k B A ) ; k = 1 , , N . ( 7 ) ##EQU00012##
[0108] Once the lag has been determined, it can be used to look up
the associated timestamp--allowing for the actual time lapse to be
calculated.
[0109] In order to obtain further system parameters, certain
physical constraints must be defined. Assuming that the average
walking speed of an adult human is between 1-1.5 ms.sup.-1, lower
and upper speed detection limits of .nu..sub.min=0.1 ms.sup.-1 and
.nu..sub.max=15 ms.sup.-1, respectively, are proposed. A speed of
zero can be deduced from a lack of correlation in the CREs at
Antenna A and Antenna B. With an antenna separation distance "d",
the shortest delay detectable (i.e. a lag of 1) is
( d 15 ) . ##EQU00013##
The longest delay that needs to be accommodated is then given
by
( d 0.1 ) . ##EQU00014##
Therefore, the minimum required size for the database is
[ ( d 0.1 ) ( d 15 ) ] + 1 = 151. ( 8 ) ##EQU00015##
[0110] I.e. N=150. The one additional slot is needed to store the
current CRE. At this point, the needed estimation frequency can be
defined. Assume a DVB-T transmitter operating in the 2K mode with 8
MHz channels is available as the RF source. The duration of each
OFDM symbol, T.sub.s, is then 224 .mu.s [11]. The estimation
frequency, (number of OFDM symbols that elapse between estimates)
is defined as
i ^ = d .upsilon. max .times. 1 T s .times. 1 .mu. ( 9 )
##EQU00016##
where .mu. is an adaptive scaling parameter that has been
introduced to improve the accuracy of the estimates
d .upsilon. max ##EQU00017##
provides the maximum time that can elapse between two estimates it
the upper speed limit of .nu..sub.max is to be detected; therefore,
dividing that quantity by T.sub.s and rounding it off to the lower
integer yields the maximum number of complete OFDM symbols that can
be allowed to pass between the two estimation instants.
[0111] .mu. is an integer that is upper bounded by
d .upsilon. max .times. 1 T s ; ##EQU00018##
this is because estimates cannot be obtained at a rate faster than
for every OFDM symbol ( =1). It is lower bounded by 1 since we are
not interested in detecting speeds that are below .nu..sub.min.
Starting with an initial value of 1, the rough speed estimates,
.nu..sub.est, are used to find the new value of .mu. to be used.
Linear interpolation is used for this purpose:
.mu. = { m .upsilon. est + ( 1 - m .upsilon. min ) , .upsilon. est
.ltoreq. ( .upsilon. max + .epsilon. ) 1 , otherwise where ( 10 ) m
= ( d .upsilon. max .times. 1 T s - 1 ) ( .upsilon. max - .upsilon.
min ) ( 11 ) ##EQU00019##
TABLE-US-00001 TABLE 1 The effect of .mu. on speeds detectable.
.sup.- = 1 .sup.- = 14 Lag Speed (m/s) Lag Speed (m/s) 1 15.944 1
223.21 2 7.9719 . . . . . . 3 5.3146 14 15.944 4 3.9860 15 14.881 .
. . . . . 16 13.951 148 0.10773 . . . . . . 149 0.10701 149 1.4981
150 0.10629 150 1.4881
and .epsilon. is a small number that is needed to allow the
adaptive procedure to converge at the upper end of speed detection
range. Its is governed by the absolute difference between
.nu..sub.max and the highest speed detectable when .mu.=1:
.epsilon. = .upsilon. max - ( d T s .times. d .upsilon. max .times.
1 T s ) . ( 12 ) ##EQU00020##
[0112] It should be clear from (10) that a high speed estimate
results in a higher value of .mu. which then causes the estimation
frequency (9) to increase--allowing a finer granularity in the time
offset estimates. As a result of the finer granularity, the
resolution at high speeds is increased*. A lower speed results in a
drop in the value of y and causes the estimation frequency to
decrease--allowing the accurate detection of lower speeds. Table 1
shows the effect of .mu. on the speeds that can be detected for
d=0.05 m. .nu..sub.max, .nu..sub.min, and T.sub.s are as described
earlier. The speed is calculated from the lag, k, using
.upsilon. est = d k .times. T s .times. i ^ . ( 13 )
##EQU00021##
*At a distance d=0.05 m, a "granularity" of 0.001 s would only
allow the detection of 10 ms.sup.-1 and 8.33 ms.sup.-1
corresponding to t=0.005 s and t=0.006 s respectively. However,
with a finer granularity of 0.0005 s, a speed of 9.09 ms.sup.-1 can
also be detected (corresponding to t=0.0055 s)--i.e. the resolution
is improved.
[0113] Table 1 clearly shows that with .mu.=1, estimation is
possible over the entire range--although the resolution at the high
end is very poor. With .mu.=14, the resolution of the estimates at
the high end is improved considerably at the expense of a reduced
range--speeds lower than 1.48 ms.sup.-1 can no longer be
detected.
[0114] It can also be seen that with .mu.=1 (the starting
condition), a true speed of 15 ms.sup.-1 is most likely to be
estimated as 15.944 ms.sup.-1. At the absence of the parameter
.epsilon., .mu. will always remain at that initial value (since the
condition .nu..sub.est.ltoreq.(.nu..sub.max+.epsilon.) in (10)
would not be satisfied) and hence the speed will continue to be
estimated as 15.944 ms.sup.-1. With .epsilon.=1, however, that
condition will be satisfied and .mu. will be increased
accordingly--increasing the estimation frequency and yielding an
estimate that is more precise than the last.
[0115] Table 1 also shows that the speed estimates available over
the entire detection range is not continuous. This is due to the
fact that the time offset estimates are always multiples of T.sub.s
and hence are themselves not continuous.
[0116] To prevent false measurements, the correlation-product with
the highest value is compared against a predefined threshold (a
suitable value can be determined empirically). FIG. 19 shows an
algorithmic flowchart of the one-dimensional & unidirectional
speed estimation algorithm.
[0117] If the algorithm is to be extended to the three-dimensional
& bidirectional case, an array with antenna pairs in each of
the three dimensions will be required. For bidirectional support,
CRE databases will have to maintained for each antenna in a pair.
Then, six independent estimation processes (two for each antenna
pair) will have to be run in parallel. The non-zero speed estimates
from each pair can then be added as a vector sum to yield the
velocity of the device in three-dimensions.
4 MATLAB IMPLEMENTATION & RESULTS
[0118] The following simplifying assumptions are made to ease the
simulator implementation: [0119] The carrier phase offsets
(.phi..sub.l), AoAs (.theta..sub.l), and time delays (.tau..sub.l)
are random but time-invariant. [0120] The channel response estimate
is exact and error free. [0121] Displacement is in one dimension
only; therefore, .PSI..sub.l,i(t) can be simplified to
[0121] .PSI..sub.l,i(t)=[x.sub.i cos(.theta..sub.l(t))].beta.
(14)
[0122] The multipath channel is modeled by ten independent
time-varying Rayleigh fading processes (implemented using MATLAB
rayleighchan channel objects) and has a maximum delay spread of 5
.mu.s. The AoA and carrier phase offsets are modeled as random
variables uniformly distributed in [0, 2.pi.]. The carrier
frequency used is 474 MHz and each OFDM symbol consists of 1705
subcarriers (DVB-T in 2K mode [11]).
[0123] The simulator developed allows the following parameters to
be set: [0124] Actual user speed in ms.sup.-1, [0125] The
correlation threshold, and [0126] The antenna separation in m.
[0127] The real speed and the estimated speed are plotted together
in realtime for comparison. FIG. 20 shows a typical simulation run
for an antenna separation of 15 cm. It can be seen that the
proposed scheme is capable of accurately estimating the speed once
the adaptive part of the algorithm converges. The adaptive
procedure is responsible for the initial fluctuations at the speed
change boundaries. From the plot it can also be seen that the
convergence time for higher speeds is lower than that for the lower
speeds; however, it is never more than approximately 0.3 s and
hence can be considered as imperceptible. At the average pedestrian
speed of 1.5 ms.sup.-1, the error in the estimate is only 0.7%.
With an antenna separation of only 15 cm, the channel responses
seen by the antennae at any given time are very similar (see FIG.
16) and hence the correlation threshold needs to be set to a high
value to prevent errors during the adaptive phase.
[0128] Simulation results for an antenna separation of 30 cm and a
lower threshold of 0.7 is shown in FIG. 21. Comparing with FIG. 20,
it can be seen that the "spikes" at the speed change boundaries are
much smaller. From this observation, it can be concluded that the
overshoots in FIG. 20 are infact due to the high level of spatial
correlation between the antennae. Once again, convergence time for
higher speeds is lower than those for lower speed and does not
exceed 0.5 s. The estimation error at 1.5 ms.sup.-1 is almost
negligible at 0.23%.
[0129] The final set of simulation results are for an antenna
separation distance of 60 cm and a threshold of 0.5 as shown in
FIG. 22. It is clear that the algorithm no longer works at this
point. The reason is revealed as soon as the channel coherence time
and the required time delay between the antennae are calculated for
a speed of 10 ms.sup.-1. The coherence time is
T c = 0.423 f d ( max ) = 0.423 .times. .lamda. .upsilon. = 0.423
.times. 3 .times. 10 8 474 .times. 10 6 10 = 0.027 s ( 15 )
##EQU00022##
while the time it takes for Antenna B to reach Antenna A is
0.6/10=0.06 s. As this time is much longer than the channel
coherence time, T.sub.c, the channel has already changed and the
CREs are no longer correlated. From a comparison of the simulations
performed, it is clear that the useful antenna separation distance
is bounded by both an upper and lower limit. If the distance is too
small, the spatial correlation between the CREs are Antenna A and
Antenna B is too high and leads to too many erroneous estimates. On
the other hand, when the distance too large, the time it takes
Antenna B to reach a position previously occupied by Antenna A
becomes longer than the coherence time of the channel and the CREs
can no longer be meaningfully cross-correlated. The usable range
appears to be approximately 5 cm-30 cm. The correlation threshold
must also be adjusted accordingly--a high threshold for low
separation and a lower threshold for higher separation
distances.
5 CONCLUSION
[0130] This paper presents a simple adaptive speed estimation
algorithm that is capable of producing high resolution estimates
within a given range. Convergence time is typically less than 0.5 s
and estimates at typical pedestrian speeds have an error of less
than 1%. The scheme utilizes relative RF signature matching and
hence does not require a priori information regarding the
environment (no large databases of exact channel estimates are
required). Also, no additional hardware would be required since
future wireless devices are expected to be MIMO capable.
[0131] Finding optimum values for the antenna separation and
threshold will be the focus of further research. A hardware
prototype is planned and is currently in development.
Acknowledgment
[0132] The authors would like to thank Dr. Sinan Sinanovic
(International University Bremen\) for his invaluable comments.
\Jacobs University Bremen as of spring 2007.
REFERENCES
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and M. Kaveh, "Level crossing rate in terms of the characteristic
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Mark, "Mobile speed estimation based on average fade slope
duration," IEEE Transactions on Communications, vol. 52, no. 12,
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Cox, "Speed estimation in wireless systems using wavelets," IEEE
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Pattern Recognition, ser. Advances in Pattern Recognition.
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Sowerby, T. S. Rappaport, and J. H. Reed, "Overview of spatial
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[0144] The following is shown in FIGS. 16 to 22:
[0145] FIG. 16 Multipath channel in time and space simulated for a
carrier frequency of 474 MHz (DVB-T) and a speed of 1.5 ms.sup.-1.
After t seconds, Antenna B is where Antenna A was and sees the same
channel response that Antenna A saw t seconds ago. As the distance,
d, between the antennae is known, the speed is easily estimated as
v=d/t.
[0146] FIG. 17 Two-antenna speed estimator setup.
[0147] FIG. 18 Buffers to hold channel response estimates for
Antenna A and Antenna B.
[0148] FIG. 19 Speed estimation algorithm. "dbA[ ]" is the CRE
buffer associated with Antenna A. The function xcorr (a, b, L)
provides the normalized cross correlation product at lag=L (i.e. it
performs the operations necessary to compute the parenthesized
expression in (7)).
[0149] FIG. 20 MATLAB simulation of one-dimensional &
unidirectional speed estimation algorithm. The threshold and the
antenna separation are set to 0.8 and 15 cm respectively.
Estimation error at a real speed of 1.5 ms.sup.-1 is approximately
0.7%.
[0150] FIG. 21 MATLAB simulation of one-dimensional &
unidirectional speed estimation algorithm. The threshold and the
antenna separation are set to 0.7 and 30 cm respectively.
Estimation error at a real speed of 1.5 ms.sup.-1 is approximately
0.23%.
[0151] FIG. 22 MATLAB simulation of one-dimensional &
unidirectional speed estimation algorithm. The threshold and the
antenna separation are set to 0.6 and 60 cm respectively.
* * * * *